We are improving CT colonography (virtual colonoscopy) by developing computer-assisted diagnosis methods. These methods attempt to identify and characterize colonic polyps automatically, thereby increasing physician accuracy and efficiency and helping patients by finding their polyps. [unreadable] [unreadable] We made a number of advances over the past year, including advances in colon and polyp segmentation, feature extraction methods, false positive reduction and classifier optimization. We improved our innovative technique to use the tenia coli (longitudinal muscle bands) as a guide for orienting the supine and prone virtual colonoscopies. This technique has broad implications for research and clinical image interpretation. We improved detection of smaller polyps by an additional 10%. We published a highly regarded observer performance study showing that computer-aided polyp detection helps radiologists locate polyps. We are at the forefront of developing computer software to analyze quality of virtual colonoscopies. Such "quality assurance" software should lead to more uniform performance in different radiologists' practices, helping patients to get the best possible imaging study.[unreadable] [unreadable] We licensed our CAD software to a company that is in the process of commercializing the software. We anticipate our research being able to help patients directly in the near future when this commercialization is successful and the technology moves from the "bench-to-the-bedside".